Combining 3D Model Contour Energy and Keypoints for Object Tracking
This work addresses object tracking challenges in computer vision, offering a combined approach that mitigates issues in existing methods, though it appears incremental in nature.
The paper tackles the problem of monocular model-based 3D object tracking by combining keypoint-based pose estimation with contour energy optimization, resulting in a method that outperforms state-of-the-art approaches on a public benchmark dataset under varied conditions.
We present a new combined approach for monocular model-based 3D tracking. A preliminary object pose is estimated by using a keypoint-based technique. The pose is then refined by optimizing the contour energy function. The energy determines the degree of correspondence between the contour of the model projection and the image edges. It is calculated based on both the intensity and orientation of the raw image gradient. For optimization, we propose a technique and search area constraints that allow overcoming the local optima and taking into account information obtained through keypoint-based pose estimation. Owing to its combined nature, our method eliminates numerous issues of keypoint-based and edge-based approaches. We demonstrate the efficiency of our method by comparing it with state-of-the-art methods on a public benchmark dataset that includes videos with various lighting conditions, movement patterns, and speed.